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SLAMF1 deregulation as a biomarker of genomic complexity and worse outcome in chronic lymphocytic leukemia.
Short title: SLAMF1 downregulation in CLL
Rigolin Gian Matteo,1* Saccenti Elena,1-2* Melandri Aurora,1 Cavallari Maurizio,1 Urso Antonio,1 Rotondo Francesco,1 Betulla Anita,1 Tognolo Lucia,1 Bardi Maria Antonella,1 Rossini Marika,1 Tammiso Elisa,1 Bassi Christian,2 Cavazzini Francesco,1 Negrini Massimo,2 Cuneo Antonio.1
1Hematology Section, Department of Medical Sciences, Azienda Ospedaliero-Universitaria, Arcispedale S. Anna, University of Ferrara, Italy
2Department of Morphology, Surgery and Experimental Medicine, and “Laboratorio per le Tecnologie delle Terapie Avanzate” (LTTA), University of Ferrara, Italy
*G.M.R. and E.S. contributed equally to this study.
Word count: 1184
Figure: 1
Table: 1. Supplemental table: 1
References: 14
Key point:
In CLL, SLAMF1 downregulation is strictly associated with genomic complexity and represents an independent predictor of inferior outcome
Recent analyses have shown that in chronic lymphocytic leukemia (CLL) karyotype complexity identifies subsets of high-risk patients. Conventional cytogenetics is the reference method to identify chromosomal abnormalities that concur to the definition of cytogenetic complexity. However, this technique is time consuming and requires specific expertise. Alternative methods or surrogate markers for genomic complexity are under evaluation. In a series of 349 CLL patients, we found lower levels of SLAMF1 expression in patients with complex karyotypes as defined by the presence of ≥5 chromosomal abnormalities (CK5; p<0.001) and in cases with major chromosomal structural abnormalities (p<0.001). A downregulation of SLAMF1 was significantly associated with CLL prognostic factors including advanced Binet stage (p=0.008), CD38 positivity (p<0.001), high levels of beta 2 microglobulin (p<0.001), IGHV unmutated status (p<0.001), 11q deletion (p<0.001), TP53 disruption (p=0.007) and higher risk CLL IPI categories (p<0.001). Multivariate analysis showed that downregulated SLAMF1 levels had negative prognostic impact on time to first treatment (p<0.001) and overall survival (p<0.001), independently of CLL-IPI, while the complex karyotype did not retain statistical significance. Our data support SLAMF1 level determination as a simple, reliable and cost-effective biomarker for genetic complexity with potential prognostic and predictive significance to be tested in larger series of patients.
In chronic lymphocytic leukemia (CLL) cytogenetic complexity emerged as an adverse independent prognostic biomarker1, 2 and for these reasons recent guidelines included conventional karyotyping as a desirable test in the work up of clinical trials.3
However, the definition of cytogenetic complexity is still a matter of debate. Recent analyses have shown that the complex karyotype as defined by the presence of ≥5 chromosomal abnormalities (CK5)4 and the occurrence of major structural abnormalities (MSA)5 in the complex karyotype might identify subsets of very high-risk patients with dismal prognosis.5, 6
Conventional cytogenetics is the reference method to identify chromosomal abnormalities that concur to the definition of cytogenetic complexity. However, this technique is time consuming and requires specific expertise. For these reasons, alternative methods or surrogate markers for genomic complexity are under evaluation.7
SLAMF1 is a self-ligand adhesion/co-stimulatory molecule that belongs to a family of 9 membrane receptors that modulates CLL responses to chemokines and regulates autophagy.8 In CLL, loss of SLAMF1 was shown to predict an unfavorable outcome9 while in CLL with complex karyotype, a downregulation of SLAMF1 was associated with MSA suggesting that SLAMF1 levels could represent a biomarker for high risk genomic complexity.5
The aim of this study is to investigate the levels of expression of SLAMF1 as a possible prognostic biomarker in CLL.
Patients. The study cohort consisted of 349 untreated CLL patients diagnosed and followed at our center between 2004 and 2019 as previously described.2 All patients were diagnosed and treated according to NCI criteria.3 The study was approved by the local ethics committee. Fludarabine or bendamustine containing regimens, with rituximab were used as first-line treatment in fit patients; chlorambucil with rituximab was used in elderly and/or unfit patients according to the treatment policy adopted at our center. Since 2015, ibrutinib, idelalisib plus rituximab or venetoclax were offered to patients.
Biological studies. Interphase FISH, cytogenetics and IGHV sequencing analyses were performed on peripheral blood (PB) samples as described.10, 11 A karyotype was defined as CK5 in the presence of ≥5 chromosome aberrations in the same clone.4 The following cytogenetic abnormalities were considered as MSA: chromosome additions, derivatives, insertions, duplications, rings, dicentric and marker chromosomes as previously reported.5 Mutations of NOTCH1, SF3B1, BIRC3 and TP53 genes were analyzed
by next generation sequencing analysis using Ion Torrent PGM (Life Technologies, Foster City, CA), as described elsewhere .11
SLAMF1 and droplet digital PCR (ddPCR). The expression of SLAMF1 was assessed within 1 year from diagnosis using the QX200TM droplet digital PCR (Bio-Rad, Hercules, CA) as described.12 ddPCR reactions were performed using Taqman Gene Expression Assay (Thermo Fisher Scientific, Waltham, Massachusetts, USA). For each gene one set of primers and a probe were chosen from the Applied Biosystems list of TaqMan® Gene Expression Assays (Hs00234149_m1 for SLAMF1 and Hs99999903_m1 for ACTB, as endogenous reference control). Synthesized cDNA was diluted 20-fold for SLAMF1 and 10.000-fold for ACTB quantification and assayed according to manufacturer’s protocol (Bio-Rad, Hercules, California, USA). A ratio SLAMF1/ACTB was finally calculated.
Statistical Analysis. The Mann-Whitney test and the Fisher exact test were applied for quantitative and categorical variables, respectively. Time to first treatment (TTFT) was calculated as the interval between diagnosis and the start of first line treatment. Overall survival (OS) was calculated from the date of diagnosis until death due to any cause or until the last patient follow-up. Survival curves were compared by the log-rank test. Proportional hazards regression analysis was used to identify the significant independent prognostic variables on TTFT and OS. The stability of the Cox model was internally validated using bootstrapping procedures. For clinical analyses, we used receiver-operating-characteristic (ROC) plot to define the best level of SLAMF1 expression that discriminated treated and untreated patients. Statistical analysis was performed using Stata 16.0 (Stata Corp, College Station, TX).
Baseline characteristics of this series are presented in supplemental table 1. A CK5 was observed in 13 cases (4%), while 39 patients (11.9%) had MSA. Eleven out of these 39 patients (28.2%) with MSA presented a CK5. A lower SLAMF1 expression was observed in patients with MSA as compared to patients without MSA (mean value 3.47, 95% CI 2.03-4.92 vs. 6.00, 95%CI 5.39-6.62, p<0.001) and in patients with MSA and CK5 as compared to patients with MSA without CK5 (mean value 1.66, 95%CI 0.39-2.93 vs. 4.83 95% CI 1.50-8.16, p =0.018). A lower SLAMF1 expression was also observed in patents with CK5 as compared to patients without CK5 (mean value 2.20, 95% CI 0.62-3.78 vs. 5.84, 95% CI 5.26-6.44, p<0.001).
To further investigate the clinical and biological impact of SLAMF1 expression, patients were subdivided into 2 groups, on the basis of the level of SLAMF1 expression that best discriminated between treated and untreated patients: those patients with a SLAMF1 level ≤2.81 were categorized as low-SLAMF1 while all other patients were considered as high-SLAMF1.
Lower levels of SLAMF1 were associated with known relevant prognostic factors (supplemental table 1) including advanced Binet stage (p=0.008), CD38 positivity (p<0.001), high levels of beta 2 microglobulin (p<0.001), IGHV unmutated status (p<0.001), 11q deletion (p<0.001), TP53 disruption (p=0.007) and higher risk CLL IPI13 categories (p<0.001). No correlation was found with NOTCH1, SF3B1 and BIRC3 mutations.
In univariate analysis, lower levels of SLAMF1 were significantly associated with a worse TTFT (p<0.001, Table 1; Figure 1A) and with a worse OS (p<0.001, Table 1 and Figure 1B). An inferior TTFT and OS were also associated with higher-risk CLL IPI prognostic scores and the CK5. Interestingly, multivariate analysis showed that SLAMF1 levels confirmed their negative prognostic impact on TTFT and OS, independently of CLL-IPI, while the CK5 did not retain statistical significance.
These observations may have relevant practical and clinical implications. The strict correlation between SLAMF1, cytogenetic complexity and high-risk genetic features suggests that SLAMF1 could represent a surrogate marker for genomic complexity that may be considered when cytogenetic analysis is not possible or is not available. Moreover, in contrast to cytogenetic analysis, SLAMF1 levels can be evaluated by relatively simple, reproducible, and affordable techniques including ddPCR and flow cytometry, as previously reported.9 in this analysis SLAMF1 expression demonstrated to be more informative and efficient than conventional cytogenetics for risk assessment. By cytogenetics only 4% patients had CK5, which means that 20 karyotypic analyses should be performed to identify 1 patient with a CK5. By contrast a lower SLAMF1 expression, as defined by ROC analysis, showed a very significant and independent prognostic impact in nearly one third of patients in a multivariate model including CLL IPI and CK5. Further studies are, however, needed to define the best and most relevant cut off for clinical applications. It is also noteworthy that SLAMF1 levels were associated with response to therapeutic agents.8 A downregulated SLAMF1, by modulating the genetic pathways that regulate chemotaxis and autophagy, may render CLL cells potentially unresponsive to autophagy-inducing drugs, including BCL2 inhibitors.9 If confirmed in prospective analyses, these observations could be of clinical utility to predict response to treatment in patients requiring therapy.14
In conclusion, our data support SLAMF1 level determination as a simple, reliable and cost-effective biomarker for genetic complexity with potential prognostic and predictive significance to be tested in larger series of patients.
This work was supported by the Fondo di Ateneo per la Ricerca (FAR) 2017, 2018, 2019 of the University of Ferrara (G.M.R., A.C., F.C.), Fondo di Incentivazione alla Ricerca (FIR) 2017 (G.M.R.), Ministero
dell’Istruzione, dell’Università e della Ricerca PRIN 2015 (A.C.; project 2015ZMRFEA), BEAT Leukemia Foundation Milan Italy (A.C.), GIlead Fellowship Award 2018 (G.M.R.) and AIL Ferrara.
Authorship Contributions
G.M.R., E.S., M.N., and A.C. conceived and designed the study; G.M.R., C.M., A.U., F.R., B.A., T.L. and C.F. acquired data and provided patient follow-up; A.M., M.A.B., E.T., E.S., M.N., C.B. and M.R. performed cytogenetic and molecular analyses; G.M.R., E.S. and A.C. analyzed and interpreted data; and all of the authors contributed to the writing, approval and review of the manuscript.
Disclosure of Conflicts of Interest
Research funding from Gilead (G.M.R.). The other authors have no competing interests.
Corresponding author
Gian Matteo Rigolin, Hematology Section, Department of Medical Sciences, Azienda Ospedaliero-Universitaria, Arcispedale S. Anna, University of Ferrara Via Aldo Moro, 8, 44124, Cona, Ferrara, Italy. Email:
1. Baliakas P, Iskas M, Gardiner A, et al. Chromosomal translocations and karyotype complexity in chronic lymphocytic leukemia: a systematic reappraisal of classic cytogenetic data. Am J Hematol. 2014;89(3):249-255.
2. Rigolin GM, Cavallari M, Quaglia FM, et al. In CLL, comorbidities and the complex karyotype are associated with an inferior outcome independently of CLL-IPI. Blood. 2017 Jun 29;129(26):3495-3498.
3. Hallek M, Cheson BD, Catovsky D, et al. iwCLL guidelines for diagnosis, indications for treatment, response assessment, and supportive management of CLL. Blood. 2018;131(25):2745-2760.
4. Baliakas P, Jeromin S, Iskas M, et al. Cytogenetic complexity in chronic lymphocytic leukemia: definitions, associations, and clinical impact. Blood. 2019;133(11):1205–1216.
5. Rigolin GM, Saccenti E, Guardalben E, et al. In chronic lymphocytic leukaemia with complex karyotype, major structural abnormalities identify a subset of patients with inferior outcome and distinct biological characteristics. Br J Haematol. 2018;181(2):229-233.
6. Visentin A, Bonaldi L, Rigolin GM, et al. The combination of complex karyotype subtypes and IGHV mutational status identifies new prognostic and predictive groups in chronic lymphocytic leukaemia. Br J Cancer. 2019;121(2):150-156.
7. Cavallari, M., Cavazzini, F., Bardi, A., et al. Biological significance and prognostic/predictive impact of complex karyotype in chronic lymphocytic leukemia. Oncotarget. 2018;9(76):34398–34412.
8. Bologna C, Deaglio S. Linking SLAMF1 to autophagy and sensitivity to therapy in chronic lymphocytic leukemia. Mol Cell Oncol. 2018;5(3):e1143077.
9. Bologna C, Buonincontri R, Serra S, et al. SLAMF1 regulation of chemotaxis and autophagy determines CLL patient response. J Clin Invest. 2016;126(1):181-194.
10. Rigolin GM, del Giudice I, Formigaro L, et al. Chromosome aberrations detected by conventional karyotyping using novel mitogens in chronic lymphocytic leukemia: Clinical and biologic correlations. Genes Chromosomes Cancer. 2015;54:818-826.
11. Rigolin GM, Saccenti E, Bassi C, et al. Extensive next-generation sequencing analysis in chronic lymphocytic leukemia at diagnosis: clinical and biological correlations. J Hematol Oncol. 2016.15;9(1):88.
12. Raponi S, Del Giudice I, Marinelli M, et al. Genetic landscape of ultra-stable chronic lymphocytic leukemia patients. Ann Oncol. 2018;29(4):966-972.
13. International CLL-IPI working group. An international prognostic index for patients with chronic lymphocytic leukaemia (CLL-IPI): a meta-analysis of individual patient data. Lancet Oncol. 2016;17(6):779-790.
14. Bosch F, Dalla-Favera R. Chronic lymphocytic leukaemia: from genetics to treatment. Nat Rev Clin Oncol. 2019;16(11):684-701.
Table 1. Univariate and multivariate analyses for TTFT and OS
Univariate analysis
Multivariate analysis
After bootstrapping HR (CI 95%) P HR (CI 95%) P HR (CI 95% P
- intermediate 3.54 (2.33-5.38) <0.001 3.10 (2.02-4.74) <0.001 3.10 (2.075-4.62) <0.001
high/very high
12.51 (8.07-19.42)
10.96 (6.83-17.56)
10.95 (7.31-16.42
<0.001 CK5 yes/no 2.05 (3.53-12.06) <0.001 1.52 (0.76-3.035) 0.235 1.52 (0.75-3.08) 0.245
SLAMF1 low/high
3.16 (2.29-4.35)
1.95 (1.38-2.76)
1.95 (1.33-2.86)
- intermediate 2.15 (1.28-3.59) 0.004 2.08 (1.22-3.57) 0.007 2.08 (1.24-3.51) 0.006
high/very high
6.93 (4.22-11.38)
6.24 (3.58-10.87)
6.24 (3.63-10.73)
<0.001 CK5 yes/no 4.39 (2.34-8.24) <0.001 1.14 (0.54-2.40) 0.734 1.14 (0.57-2.29) 0.718
SLAMF1 low/high
2.59 (1.76-3.80)
1.61 (1.04-2.49)
1.61 (1.06-2.48)
HR: hazard ratio; CI: confidence interval
Legend to figure.
Figure 1. TTFT (in A) and OS (in B) according to SLAMF1 levels.

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